Solvent Effects on Radical Copolymerization Kinetics of 2-Hydroxyethyl Methacrylate and Butyl Methacrylate
Bibliographic record
Abstract
2-Hydroxyethyl methacrylate (HEMA) is an important component of many acrylic resins used in coatings formulations, as the functionality ensures that the chains participate in the cross-linking reactions required to form the final product. Hence, the knowledge of their radical copolymerization kinetic coefficients is vital for both process and recipe improvements. The pulsed laser polymerization (PLP) technique is paired with size exclusion chromatography (SEC) and nuclear magnetic resonance (NMR) to provide kinetic coefficients for the copolymerization of HEMA with butyl methacrylate (BMA) in various solvents. The choice of solvent has a significant impact on both copolymer composition and on the composition-averaged propagation rate coefficient (kp,cop). Compared to the bulk system, both n-butanol and dimethylformamide reduce the relative reactivity of HEMA during copolymerization, while xylene as a solvent enhances HEMA reactivity. The magnitude of the solvent effect varies with monomer concentration, as shown by a systematic study of monomer/solvent mixtures containing 50 vol%, 20 vol%, and 10 vol% monomer. The observed behavior is related to the influence of hydrogen bonding on monomer reactivity, with the experimental results fit using the terminal model of radical copolymerization to provide estimates of reactivity ratios and kp,HEMA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".